Mobile Robot Control 2023 HAL-9000: Difference between revisions
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====Local path planning - Artificial Potential Field (Xingyi & Guglielmo)==== | ====Local path planning - Artificial Potential Field (Xingyi & Guglielmo)==== | ||
The Artificial Potential Field method has been chosen as a local path planning algorithm. The main idea behind it is to model obstacles and the goal as, respectively, repulsive and attractive vector fields. From a potential point of view this implies assigning a low potential to the goal and a large potential to the obstacles. According to these potential fields, the robot is expected to move autonomously towards the lowest potential coordinates. | |||
Basing on the resulting potential of the robot’s position, the induced velocities can be computed and sent to the robot as references for the navigation. Practically speaking, the APF was designed as a function that given the current pose of the robot, the goal pose and the laser data, returns the reference velocities needed to move towards the minimum potential position. | |||
The laser data is used to model obstacles. One easy approach could be to just take into account the closest obstacle represented as a point in the space. Its relative position with respect to the robot would be given by the range and the angle of the minimum laser scan. This approach would however not be robust, since a single laser measurement is not robust and thus the obstacle position would be uncertain. To tackle this, every laser measurement below a fixed detection threshold is chosen to represent an obstacle. In this way, every obstacle is modeled as a point cloud, of which every point generates a repulsive velocity reference for the robot. The overall repulsive velocity is then the sum of all these point-generated velocity, considering the orientation of those. Summing up these values allows the algorithm to find the right balance between forces pointing in different directions. It also fights the uncertainty of the laser data compensating for outliers. | |||
Calling the APF function until the goal is reached allows the robot to get to the destination avoiding both static and dynamic obstacles. This is due to the fact that the obstacle set is re-calculated every time the function is called, dealing with errors or moving objects. | |||
The goal is just a point in the map as well, thus the attractive velocity is unique. To address this, all the parameters have been tuned to find the right balance between the attractive and the repulsive action. | |||
The pros of this approach: | |||
- It can deal with all kinds of obstacles; | |||
- It is robust with respect to uncertainties in the measurements; | |||
- It is able to find a smooth path towards the goal. | |||
The cons of this approach: | |||
- It relies on good localization: the attractive force is, in fact, a function of the current pose and the goal pose; | |||
- It is intrinsically affected by local minima issues, leading the robot to a low-potential position which might not be the goal; | |||
- It relies on parameter tuning: all the velocity coefficients and al the thresholds must be found through a trial-and-error process. | |||
Revision as of 16:54, 30 June 2023
Group members:
| Name | student ID |
|---|---|
| Xingyi Li | 1820443 |
| Alexander De Pauw | 1877267 |
| Timo van Gerwen | 1321854 |
| Yuzhou Nie | 1863428 |
| Ronghui Li | 1707183 |
| Guglielmo Morselli | 1959301 |
Design Presentation:
https://cstwiki.wtb.tue.nl/wiki/File:Midterm_presentation.pdf
Tasks Divide:
Data-flow Diagram& State Diagram:
[[File:|center|thumb|600px]]
Towards Final Challenge
Introduction (Ronghui)
// introduction of challenge
Strategy (Timo)
// What do we aim for and how do we want to do it
// Table sequence
Algorithms
// algorithms used for the separate blocks
Global path planning - A* (Alex)
// A* algorithm
Local path planning - Artificial Potential Field (Xingyi & Guglielmo)
The Artificial Potential Field method has been chosen as a local path planning algorithm. The main idea behind it is to model obstacles and the goal as, respectively, repulsive and attractive vector fields. From a potential point of view this implies assigning a low potential to the goal and a large potential to the obstacles. According to these potential fields, the robot is expected to move autonomously towards the lowest potential coordinates.
Basing on the resulting potential of the robot’s position, the induced velocities can be computed and sent to the robot as references for the navigation. Practically speaking, the APF was designed as a function that given the current pose of the robot, the goal pose and the laser data, returns the reference velocities needed to move towards the minimum potential position.
The laser data is used to model obstacles. One easy approach could be to just take into account the closest obstacle represented as a point in the space. Its relative position with respect to the robot would be given by the range and the angle of the minimum laser scan. This approach would however not be robust, since a single laser measurement is not robust and thus the obstacle position would be uncertain. To tackle this, every laser measurement below a fixed detection threshold is chosen to represent an obstacle. In this way, every obstacle is modeled as a point cloud, of which every point generates a repulsive velocity reference for the robot. The overall repulsive velocity is then the sum of all these point-generated velocity, considering the orientation of those. Summing up these values allows the algorithm to find the right balance between forces pointing in different directions. It also fights the uncertainty of the laser data compensating for outliers.
Calling the APF function until the goal is reached allows the robot to get to the destination avoiding both static and dynamic obstacles. This is due to the fact that the obstacle set is re-calculated every time the function is called, dealing with errors or moving objects.
The goal is just a point in the map as well, thus the attractive velocity is unique. To address this, all the parameters have been tuned to find the right balance between the attractive and the repulsive action.
The pros of this approach:
- It can deal with all kinds of obstacles;
- It is robust with respect to uncertainties in the measurements;
- It is able to find a smooth path towards the goal.
The cons of this approach:
- It relies on good localization: the attractive force is, in fact, a function of the current pose and the goal pose;
- It is intrinsically affected by local minima issues, leading the robot to a low-potential position which might not be the goal;
- It relies on parameter tuning: all the velocity coefficients and al the thresholds must be found through a trial-and-error process.
Localization - Particle filter (Yuzhou)
// Particle filter
Integration (Xingyi & Guglielmo)
// How we have combined the blocks
Performance (Ronghui)
// Explain what performance is reached
// Include simulation vs final challenge test
Deficiencies
// What did not work
Conclusion
// Conclusions on project
Recommendations (Timo)
// Recommendations and next steps
openDoor function